Analyzing transit service reliability using detailed data from automatic vehicular locator systems
Bibliographic record
Abstract
Abstract The widespread adoption of automated vehicle location (AVL) systems and automatic passenger counters (APCs) in the transit industry has opened new venues in operations and system monitoring. In 2005, Metro Transit, Minnesota, implemented AVL system and partially implemented APC technologies. To date there has been little effort to employ the collected data in evaluating transit performance. This research uses such data to assess performance issues along a cross‐town route in the Metro Transit system. We generate a series of visual and analytical analyses to predict run time, schedule adherence and reliability of the transit route at two scales: the time point segment and the route level to demonstrate ways of identifying causes of decline in reliability levels. The analytical models show that while headways are maintained, schedule revisions are needed to improve run time and schedule adherence. Finally, the analysis suggests that many scheduled stops along this route are underutilized and recommends stop consolidation as a tool to decrease variability of service through concentrating passenger demand along a fewer number of stops. Copyright © 2010 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".